In recent years, text-guided image manipulation has gained increasing attention in the image generation research field. Recent works have proposed to deal with a simplified setting where the input image only has a single object and the text modification is acquired by swapping image captions or labels. In this paper, we study a setting that allows users to edit an image with multiple objects using complex text instructions. In this image generation task, the inputs are a reference image and an instruction in natural language that describes desired modifications to the input image. We propose a GAN-based method to tackle this problem. The key idea is to treat text as neural operators to locally modify the image feature. We show that the proposed model performs favorably against recent baselines on three public datasets.
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